This study aims to analyze effectiveness of the resource use under the total allowable catch system (TACs) of Comb pen shell, a species among TAC targeting ones through its stock assessment based on the surplus production model such as the Clark·Yoshimoto·Pooley (CYP) model. Particularly, this study is separated into five analysis periods in order to understand changes in Comb pen shell resource and its efficient use after TAC system implemented in 2001. The results of this study are as follows. First, five sustainable yield curves (SYCs) and exponential growth functions (EGFs) produced by the surplus production model based on Gompertz growth function to compare before and after implementation of the Korean TAC system show that the TAC system has generated a positive stock rebuilding effect for Comb pen shell caught by the diver fishery since 2001. Secondly, five profits based on differences between the sustainable total revenue (STR) and the total cost (TC) with respect to fishing efforts present that the TAC system has increased efficiency of resource use of Comb pen shell caught by the diver fishery after implementation of the Korean TAC system. In conclusion, the Korean TAC system has increased efficiency of resource use as well as has led a positive stock rebuilding effect for Comb pen shell.

Using artificial neural network (ANN) technique, auction prices for common mackerel were forecasted with the daily total sale and auction price data at the Busan Cooperative Fish Market before introducing Total Allowable Catch (TAC) system, when catch data had no limit in Korea. Virtual input data produced from actual data were used to improve the accuracy of prediction and the suitable neural network was induced for the prediction. We tested 35 networks to be retained 10, and found good performance network with regression ratio of 0.904 and determination coefficient of 0.695. There were significant variations between training and verification errors in this network. Ideally, it should require more training cases to avoid over-learning, which leads to improve performance and makes the results more reliable. And the precision of prediction was improved when environmental factors including physical and biological variables were added. This network for prediction of price and catch was considered to be applicable for other fishes.